Graciela Nora Doz

Wire breakage alters strand continuity, interwire contact, load redistribution, and modal response. This study evaluates normalized frequency changes as a laboratory fingerprint of persistent mechanical changes in unloaded seven-wire helical strands. Six 12.7 mm, 1 + 6 ASTM A586-18 strands with free lengths of 200 mm (S20) and 400 mm (S40) were tested in tension. Dynamic measurements comprised five repeated impacts on one strand per length in the intact state, after the elastoplastic transition, and during progressive wire breakage. Tensile records and optical observations documented nonsimultaneous fracture and intermediate load-bearing configurations. After unloading from the elastoplastic transition, the fundamental flexural frequency reductions were 5.6% and 6.3%, compared with a maximum repeat-impact coefficient of variation of 0.3%. A maximum reduction of 18% was reported during the wire-break sequence. A comparison using the same experimental summaries shows that direct frequency shift and the squared-frequency index yield identical detection decisions under corresponding thresholds. Sensitivity calculations quantify the effect of the threshold and modal-mass assumption, and published strand experiments provide an external comparison. The contribution is an experimentally grounded interpretation of residual frequency changes as a global state indicator. The results support a laboratory proof-of-concept for the two tested strands, with equivalent stiffness loss interpreted under explicit modal assumptions.

This paper presents a staggered adaptive global–local finite element formulation for helically wound steel strands under cyclic loading. The gap addressed is the absence of a framework that links global strand deformation, local interwire contact, and degradation feedback to effective stiffness, tangent operators, damping, and modal evolution. The method couples a geometrically nonlinear global model, a Hertz–Coulomb local contact submodel with cycle-dependent damage, explicit scale-transfer/projection operators, and neural-network-guided adaptive refinement. Local frictional dissipation is projected to the structural scale to update stiffness, damping, and modal operators incrementally. Verification on seven-wire strands with free lengths of 200 and 400 mm uses quasi-static force–displacement curves, free-decay responses, deformation–damage indicators, and fracture evidence. The formulation yields secant-modulus biases within ±3.1%, peak-load discrepancies below 6%, quasi-static RMSE below 4%, and frequency errors below 1.2%. Damage localizes mainly in the outer helical wires while the core preserves residual load-carrying capacity, explaining the nonuniform stiffness loss and modal shifts. The results support the hypothesis that consistent global-to-local transfer, damage-feedback projection, and adaptive refinement capture the localized mechanisms governing strand degradation without uniform refinement or empirical recalibration. The main contribution is a traceable causal link between local contact mechanics and global static/dynamic indicators for vibration-based assessment of cable-supported systems.

The natural and accidental deterioration of civil structures, combined with the increasing complexity of engineering projects, demands continuous monitoring strategies to ensure structural safety and durability. In this context, Structural Health Monitoring (SHM) techniques based on variations in modal properties have been widely investigated due to their capability for global assessment. However, practical limitations—such as information loss during modal identification and the low sensitivity of certain modal parameters to damage—have motivated the development of alternative approaches. Among these approaches, methodologies based on statistical features extracted directly from acceleration time series have gained prominence, particularly when combined with Machine Learning (ML) classifiers. Although several studies have successfully employed statistical indicators for damage detection, the relative relevance of these indicators and the potential benefits of dimensionality reduction can still be explored through a systematic evaluation, particularly under Sim-to-Real conditions. This study investigates the performance of ten statistical indicators as input features for ML-based damage detection, applied to an experimentally tested frame structure, using numerically obtained data for training. A structured variable analysis was conducted to assess the individual and combined impact of the indicators, supported by visualization techniques and classifier performance metrics. The results showed some possible redundancies between certain indicators and demonstrated that a reduced set of attributes can achieve comparable or superior classification performance, while simultaneously improving training stability and computational efficiency. These findings highlight the importance of systematic feature selection and the effectiveness of reduced-dimensional statistical representations for SHM applications in Sim-to-Real scenarios.

Efficient structural health monitoring requires not only robust computational strategies but also reliable data acquisition systems capable of capturing representative dynamic responses of real structures. In this study, a continuous dynamic monitoring system composed of accelerometers strategically distributed along the bridge deck provides the foundational data for all subsequent computational analyses. The integrated application of t-Distributed Stochastic Neighbor Embedding (t-SNE) and Learning Vector Quantization (LVQ) is evaluated for the identification of structural damage in the Infante D. Henrique Bridge, located in Porto, Portugal. Data obtained from five years of continuous monitoring were used, with a portion of the identified natural frequencies employed for training and validation of the LVQ algorithm. The robustness of the approach was assessed through artificial modification of data from the second year of monitoring, simulating different damage scenarios. The results demonstrate that the t-SNE–LVQ combination improves discrimination between normal and damaged structural states, achieving identification rates above 70%. The main contribution of this work lies in demonstrating the feasibility and effectiveness of an integrated hardware-to-software machine learning framework applied to real monitoring data, highlighting its potential for structural health monitoring and decision-support systems.

A routine for damage detection based on multiple damage modelling and artificial neural networks (ANNs) is presented in this article. The routine aims to identify the existence and location of damage, along with the most damaged elements. The first step in the routine is to define ‘critical’ regions based on stresses and deformations of the updated numerical model of the structure, as well as the probable order of occurrence in the case of multiple damage by simulating different scenarios. The second step is to train the ANN using the natural frequencies of intact and damaged numerical models as input data, with vectors indicating the position and magnitude of the damage as the expected output. A feed-forward back-propagation ANN was adopted. The approach was used to evaluate a cable-stayed footbridge. A new dynamic test was performed and a vector composed of the identified frequencies was input into the network, which indicated the existence of possible damage. The provided damage scenario was applied to the updated model, along with the experimentally obtained prestressing forces of the stays. The maximum difference between the frequencies of the updated and damaged numerical model with the damage vector provided by the network and the frequencies experimentally identified in the new test was 1.80%.

A number of techniques have been proposed for the detection of damage to structures. Some of these techniques are based on vibrational data, whereby changes in physical properties can be identified through alterations in vibrational characteristics. The majority of techniques are based on variations of modal parameters. The direct utilization of accelerations offers considerable advantages in damage detection, making the process more rapid and more straightforward by eliminating the necessity for an identification stage. The paucity of research into raw dynamic data (accelerations) can be attributed to the considerable volume of data that must be stored and manipulated. Some authors suggest Symbolic Data Analysis (SDA) and Principal Components Analysis (PCA) as techniques for reducing the volume of data without compromising its fundamental characteristics. The objective of this study is to examine the number of categories utilized in SDA and the number of principal components in PCA that yielded the optimal damage state classification results when applied to experimental data from a simply supported beam and numerical data from a four-story Benchmark. Two techniques for determining the number of SDA categories are put forth for consideration.

The bi-axial hollow slab is the result of applying a technology that removes concrete from the least useful region and replaces it with hollow plastic spheres. This technique leads to material economy and self-weight saving, without considerable inertial loss, enabling larger and more flexible spans. Consequently, these spans are vulnerable to unwanted vibrations and dynamic acceleration caused by external actions, such as movements produced by human activity. This way, understanding the dynamic behavior of this technology and confronting it with normative limits on the project phase is essential for a good performance in the serviceability limit state, bringing comfort to the user. This paper studies the dynamic behavior of a bi-axial hollow slab submitted to human actions, such as jumping, dancing, clapping, moving the body, and moving and clapping at the same time. For that, this work made a numerical model of the structure and applied dynamic actions to it. The numerical results show that, for most of the actions, normative limits are satisfied, but the slab presents excessive accelerations in the case of jumping and dancing.

This paper presents an investigation on the structural condition of a cable-stayed footbridge based on data collected in two dynamic tests, performed at different times. The natural frequencies and mode shapes were obtained by applying output only modal identification methods. At least four experimental mode shapes of the footbridge and stay cables were identified in Operational Modal Analysis. A 3D finite element model of the footbridge was calibrated by adjusting its dynamic properties to the experimentally identified in the first test. The procedure showed, besides an approximation of the dynamic behavior, an approximation of its static behavior, which was confronted to the structural design. The analysis found a discrepancy between the required reinforcement based on the updated model stresses and the existing steel reinforcement. The second test, performed one year later, provided different natural frequency values to those found in the first test and the spectrum presented inconsistency in one of its peaks. It was concluded that the structure presents conditioning problems, thus warning to future structural problems.

In many urban buildings, there is the presence of gas central storage, which contain pressurized tanks. One of the main risks in these places is an explosion, which may or may not is followed by fire. In general, the shock wave formed by this phenomenon is disastrous and can cause material damage and even fatalities. Recent research has contributed to understanding protective systems against this phenomenon; however, it is necessary to advance in developing protection devices for gas central storage. From this perspective, the implementation plan of these devices in order to mitigate the harmful effects of explosions are essential to raise the safety level in construction. Physical protection barriers are appropriate solutions for the protection of buildings, especially in places where it is impossible to bury gas reservoirs. In addition to the potential to reduce back pressure levels, protective walls, when properly designed, can also prevent the spread of debris from the explosion. Another kind of barrier that influences the propagation of the shock wave close to the ground is the ditches, whose function is related to the absorption and redirection of wave energy. Understanding the positioning, geometry, and overpressure attenuation potential of physical barriers are essential in developing safer and more reliable projects. This article aims to study protective barriers in buildings with pressurized gas tanks. The study was developed numerically using the Autodyn software. This program is a tool based on computational fluid dynamics (CFD). The protective capacity of the proposed types of protection regarding the mitigation of incident overpressures was evaluated qualitatively and quantitatively.